Generative AI development blog
Plain-language guides on building LLM applications, RAG systems, and AI agents that hold up in production.
4 min read
Why RAG answers fail: chunking and retrieval
Many wrong RAG answers trace to retrieval, not the model. Seven documented failure points, how chunking and search cause them, and how to find yours.
4 min read
Hosted or open-weight LLM: how to choose
Should your generative AI project call a hosted model API or run an open-weight model yourself? The factors that decide it and how to test candidates fairly.
4 min read
What a generative AI system costs to run after launch
The line items in the monthly cost of a generative AI system, a worked estimate you can adapt, and ways to lower model spend while checking quality holds.
4 min read
AI agents vs workflows: which one to build
An AI agent lets the model choose its own steps; a workflow follows steps you wrote. How reliability, cost, and reversibility should decide which one you build.
4 min read
How to evaluate a RAG system before you ship it
Test retrieval and generation separately. How to build an evaluation set, which checks to run, and how to set a launch threshold before a RAG system ships.
3 min read
What is generative AI development?
Generative AI development means building software around models that write, summarize, and act. What the work involves and where projects tend to fail.
4 min read
RAG vs fine-tuning: which one your project needs
RAG changes what a model can see. Fine-tuning changes how it behaves. How to tell which problem you have, what the research says, and when to use both.
3 min read
How to choose a generative AI development company
Six questions to ask a generative AI development company before you sign, what a good answer sounds like, and the warning signs that mean you should walk away.
Where to start
New to the field? Read these in order. Each one links to the research it rests on.
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